You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Product type Paperback

Published in Mar 2022

Publisher Packt

ISBN-13 9781803247335

Length 602 pages

Edition 2nd Edition

Languages

Processing

Tools

Processing

Concepts

Mobile Application Development

Author (1):

Denis Rothman

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Table of Contents (25) Chapters

Preface

1. What are Transformers?

2. Getting Started with the Architecture of the Transformer Model FREE CHAPTER

3. Fine-Tuning BERT Models

4. Pretraining a RoBERTa Model from Scratch

5. Downstream NLP Tasks with Transformers

6. Machine Translation with the Transformer

7. The Rise of Suprahuman Transformers with GPT-3 Engines

8. Applying Transformers to Legal and Financial Documents for AI Text Summarization

9. Matching Tokenizers and Datasets

10. Semantic Role Labeling with BERT-Based Transformers

11. Let Your Data Do the Talking: Story, Questions, and Answers

12. Detecting Customer Emotions to Make Predictions

13. Analyzing Fake News with Transformers

14. Interpreting Black Box Transformer Models

15. From NLP to Task-Agnostic Transformer Models

16. The Emergence of Transformer-Driven Copilots

17. The Consolidation of Suprahuman Transformers with OpenAI’s ChatGPT and GPT-4

18. Other Books You May Enjoy

19. Index

Appendix I — Terminology of Transformer Models

1. Appendix II — Hardware Constraints for Transformer Models

2. Appendix III — Generic Text Completion with GPT-2

3. Appendix IV — Custom Text Completion with GPT-2

4. Appendix V — Answers to the Questions

The Emergence of Transformer-Driven Copilots

When Industry 4.0 (I4.0) reaches maturity, it will all be about machine-to-machine connections, communication, and decision-making. AI will be primarily embedded in ready-to-use pay-as-you-go cloud AI solutions. Big tech will absorb the most talented AI specialists to create APIs, interfaces, and integration tools.

AI specialists will go from development to design to becoming architects, integrators, and cloud AI pipeline administrators. Thus, AI is becoming a job for engineer consultants more than engineer developers.

Chapter 1, What Are Transformers?, introduced foundation models, transformers that can do NLP tasks they were not trained for. Chapter 15, From NLP to Task-Agnostic Transformer Models, expanded foundation model transformers to task-agnostic models that can perform vision tasks, NLP tasks, and much more.

This chapter will extend task-agnostic OpenAI GPT-3 models to a wide range of copilot tasks. A new generation...

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You're reading from Transformers for Natural Language Processing Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4

Table of Contents (25) Chapters

The Emergence of Transformer-Driven Copilots

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Authors (1)